code: def robins_alexander_clustering(G):
      r"""Compute the bipartite clustering of G.
  
      Robins and Alexander [1]_ defined bipartite clustering coefficient as
      four times the number of four cycles `C_4` divided by the number of
      three paths `L_3` in a bipartite graph:
  
      .. math::
  
         CC_4 = \frac{4 * C_4}{L_3}
  
      Parameters
      ----------
      G : graph
          a bipartite graph
  
      Returns
      -------
      clustering : float
         The Robins and Alexander bipartite clustering for the input graph.
  
      Examples
      --------
      >>> from networkx.algorithms import bipartite
      >>> G = nx.davis_southern_women_graph()
      >>> print(round(bipartite.robins_alexander_clustering(G), 3))
      0.468
  
      See Also
      --------
      latapy_clustering
      networkx.algorithms.cluster.square_clustering
  
      References
      ----------
      .. [1] Robins, G. and M. Alexander (2004). Small worlds among interlocking
             directors: Network structure and distance in bipartite graphs.
             Computational & Mathematical Organization Theory 10(1), 69–94.
  
      """
      if G.order() < 4 or G.size() < 3:
          return 0
      L_3 = _threepaths(G)
      if L_3 == 0:
          return 0
      C_4 = _four_cycles(G)
      return (4.0 * C_4) / L_3
  
file_path: networkx/networkx/algorithms/bipartite/cluster.py
imports: import itertools
  import networkx
label_desc: Use image node_id 6 for calling a global function with example usage: robins_alexander_clustering(G) and returns: unknown, int, int